Data and the use of data in business decision making is ubiquitous. The need for data analytics is an ever-growing, increasingly important, skill set for both the current and future workforce.
蘑菇TV's Master of Science in Data Analytics degree is an online STEM-designated program that prepares students to meet 鈥楤ig Data鈥� business challenges. Graduates emerge well-prepared to apply data analysis, visualization, and artificial intelligence prediction tools to create maximum value and expand market opportunities for organizations.
Along with a core data analytics curriculum, students select one area of business concentration.
This 30-credit-hour course is offered through asynchronous online study. That means you study on your own time, offering the ultimate level of flexibility for the working professional who is looking to advance their career with a graduate degree.
Other benefits of OCU's Master of Science in Data Analytics program include:
A course designed to teach the fundamentals of programming as it relates to the development of data science and analytics solutions. May include software platforms Python or R.
Focuses on the identification and access of information sources and analyzing the information to make informed decisions and solve managerial problems. Among the topics included are numerical and graphic description of data, confidence intervals, hypothesis testing, regression analysis and predictive modeling, linear allocation models and allocating resources, forecasting, and decision analysis. The course utilizes spreadsheet, statistical and simulation software.
A course on communication of analytical and data science results using visualization software. May include Tableau, PowerBi and other visualization packages.
An integrated study of systems for collecting, storing and retrieving data with a particular emphasis on relational databases. May include Snowflake, SQL SERVER, MySQL.
Addresses tools and techniques required for analyzing business data for forecasting. Includes time series analysis and time series forecasting, and application of these techniques to support business decision makers.
Addresses practices related to predictive modeling (decision tree, regression, neural network, ensemble and boosting models, among others). Includes modifying data for better analysis results, model training and testing, machine learning methods, comparing and explaining complex models, generating predictions and communicating results to help make better business decisions.
Addresses concepts, tool and techniques for using large datasets to address business problems. Includes understanding big data concepts, common architectures, and using industry-standard tools to store, query, transform and analyze large datasets. Techniques related to importing and working with diverse types of data across different technical environments discussed and practiced.
To complete the 30-credit-hour degree requirements, students can choose from nine credit hours from existing disciplines including MS in Accounting, MS in Financial Analytics, MBA in Leadership and MBA in Health Care Administration.
Prerequisites are not required.
Applicants must demonstrate a basic understanding of analytical techniques and have strong communication skills.
Applications are evaluated on an individual basis considering the following factors:
1. Academic Background (Official undergraduate transcript sent directly by degree-granting institution)
2. Professional Experience (Resume or CV)
For questions about the application process:
Cindy Treadway
Meinders Graduate Advisor
[email protected]
405-208-5154
For questions about the international application process:
Aaron Wheelbarger
Director of International Admissions
[email protected]
405-208-5006
